Empirical analysis of rough set categorical clustering techniques based on rough purity and value set
Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, attention has been put on categorical data clustering, where data objects are made up of non-numerical attributes. The implementation of several existing categorical clustering techniques is c...
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| الوصول للمادة أونلاين: | http://eprints.uthm.edu.my/336/1/JAMAL%20UDDIN%20WATERMARK.pdf http://eprints.uthm.edu.my/336/2/24p%20JAMAL%20UDDIN.pdf |
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my-uthm-ep.3362021-07-22T07:09:41Z Empirical analysis of rough set categorical clustering techniques based on rough purity and value set 2017-08 Uddin, Jamal QA76 Computer software Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, attention has been put on categorical data clustering, where data objects are made up of non-numerical attributes. The implementation of several existing categorical clustering techniques is challenging as some are unable to handle uncertainty and others have stability issues. In the process of dealing with categorical data and handling uncertainty, the rough set theory has become well-established mechanism in a wide variety of applications including databases. The recent techniques such as Information-Theoretic Dependency Roughness (ITDR), Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR), Min-Min Roughness (MMR), and standard-deviation roughness (SDR). This work explores the limitations and issues of ITDR, MDA and MSA techniques on data sets where these techniques fails to select or faces difficulty in selecting their best clustering attribute. Accordingly, two alternative techniques named Rough Purity Approach (RPA) and Maximum Value Attribute (MVA) are proposed. The novelty of both proposed approaches is that, the RPA presents a new uncertainty definition based on purity of rough relational data base whereas, the MVA unlike other rough set theory techniques uses the domain knowledge such as value set combined with number of clusters (NoC). To show the significance, mathematical and theoretical basis for proposed approaches, several propositions are illustrated. Moreover, the recent rough categorical techniques like MDA, MSA, ITDR and classical clustering technique like simple K-mean are used for comparison and the results are presented in tabular and graphical forms. For experiments, data sets from previously utilized research cases, a real supply base management (SBM) data set and UCI repository are utilized. The results reveal significant improvement by proposed techniques for categorical clustering in terms of purity (21%), entropy (9%), accuracy (16%), rough accuracy (11%), iterations (99%) and time (93%). vii 2017-08 Thesis http://eprints.uthm.edu.my/336/ http://eprints.uthm.edu.my/336/1/JAMAL%20UDDIN%20WATERMARK.pdf text en validuser http://eprints.uthm.edu.my/336/2/24p%20JAMAL%20UDDIN.pdf text en public phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat |
| institution |
Universiti Tun Hussein Onn Malaysia |
| collection |
UTHM Institutional Repository |
| language |
English English |
| topic |
QA76 Computer software |
| spellingShingle |
QA76 Computer software Uddin, Jamal Empirical analysis of rough set categorical clustering techniques based on rough purity and value set |
| description |
Clustering a set of objects into homogeneous groups is a fundamental operation
in data mining. Recently, attention has been put on categorical data clustering,
where data objects are made up of non-numerical attributes. The implementation of
several existing categorical clustering techniques is challenging as some are unable
to handle uncertainty and others have stability issues. In the process of dealing
with categorical data and handling uncertainty, the rough set theory has become
well-established mechanism in a wide variety of applications including databases.
The recent techniques such as Information-Theoretic Dependency Roughness (ITDR),
Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA)
outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness
(TR), Min-Min Roughness (MMR), and standard-deviation roughness (SDR). This
work explores the limitations and issues of ITDR, MDA and MSA techniques on
data sets where these techniques fails to select or faces difficulty in selecting their
best clustering attribute. Accordingly, two alternative techniques named Rough Purity
Approach (RPA) and Maximum Value Attribute (MVA) are proposed. The novelty
of both proposed approaches is that, the RPA presents a new uncertainty definition
based on purity of rough relational data base whereas, the MVA unlike other rough
set theory techniques uses the domain knowledge such as value set combined with
number of clusters (NoC). To show the significance, mathematical and theoretical
basis for proposed approaches, several propositions are illustrated. Moreover, the
recent rough categorical techniques like MDA, MSA, ITDR and classical clustering
technique like simple K-mean are used for comparison and the results are presented
in tabular and graphical forms. For experiments, data sets from previously utilized
research cases, a real supply base management (SBM) data set and UCI repository
are utilized. The results reveal significant improvement by proposed techniques for
categorical clustering in terms of purity (21%), entropy (9%), accuracy (16%), rough
accuracy (11%), iterations (99%) and time (93%).
vii |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Uddin, Jamal |
| author_facet |
Uddin, Jamal |
| author_sort |
Uddin, Jamal |
| title |
Empirical analysis of rough set categorical clustering techniques based on rough purity and value set |
| title_short |
Empirical analysis of rough set categorical clustering techniques based on rough purity and value set |
| title_full |
Empirical analysis of rough set categorical clustering techniques based on rough purity and value set |
| title_fullStr |
Empirical analysis of rough set categorical clustering techniques based on rough purity and value set |
| title_full_unstemmed |
Empirical analysis of rough set categorical clustering techniques based on rough purity and value set |
| title_sort |
empirical analysis of rough set categorical clustering techniques based on rough purity and value set |
| granting_institution |
Universiti Tun Hussein Onn Malaysia |
| granting_department |
Fakulti Sains Komputer dan Teknologi Maklumat |
| publishDate |
2017 |
| url |
http://eprints.uthm.edu.my/336/1/JAMAL%20UDDIN%20WATERMARK.pdf http://eprints.uthm.edu.my/336/2/24p%20JAMAL%20UDDIN.pdf |
| _version_ |
1747830585522388992 |